# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Keras image preprocessing layers."""


import numpy as np
import tensorflow.compat.v2 as tf

from keras import backend
from keras.engine import base_layer
from keras.engine import base_preprocessing_layer
from keras.layers.preprocessing import preprocessing_utils as utils
from keras.utils import image_utils
from keras.utils import tf_utils

# isort: off
from tensorflow.python.ops import stateless_random_ops
from tensorflow.python.util.tf_export import keras_export
from tensorflow.tools.docs import doc_controls

H_AXIS = -3
W_AXIS = -2

IMAGES = "images"
LABELS = "labels"
TARGETS = "targets"
BOUNDING_BOXES = "bounding_boxes"


def check_fill_mode_and_interpolation(fill_mode, interpolation):
    if fill_mode not in {"reflect", "wrap", "constant", "nearest"}:
        raise NotImplementedError(
            "Unknown `fill_mode` {}. Only `reflect`, `wrap`, "
            "`constant` and `nearest` are supported.".format(fill_mode)
        )
    if interpolation not in {"nearest", "bilinear"}:
        raise NotImplementedError(
            "Unknown `interpolation` {}. Only `nearest` and "
            "`bilinear` are supported.".format(interpolation)
        )


@keras_export(
    "keras.layers.Resizing", "keras.layers.experimental.preprocessing.Resizing"
)
class Resizing(base_layer.Layer):
    """A preprocessing layer which resizes images.

    This layer resizes an image input to a target height and width. The input
    should be a 4D (batched) or 3D (unbatched) tensor in `"channels_last"`
    format.  Input pixel values can be of any range (e.g. `[0., 1.)` or `[0,
    255]`) and of interger or floating point dtype. By default, the layer will
    output floats.

    This layer can be called on tf.RaggedTensor batches of input images of
    distinct sizes, and will resize the outputs to dense tensors of uniform
    size.

    For an overview and full list of preprocessing layers, see the preprocessing
    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).

    Args:
      height: Integer, the height of the output shape.
      width: Integer, the width of the output shape.
      interpolation: String, the interpolation method. Defaults to `"bilinear"`.
        Supports `"bilinear"`, `"nearest"`, `"bicubic"`, `"area"`, `"lanczos3"`,
        `"lanczos5"`, `"gaussian"`, `"mitchellcubic"`.
      crop_to_aspect_ratio: If True, resize the images without aspect
        ratio distortion. When the original aspect ratio differs from the target
        aspect ratio, the output image will be cropped so as to return the
        largest possible window in the image (of size `(height, width)`) that
        matches the target aspect ratio. By default
        (`crop_to_aspect_ratio=False`), aspect ratio may not be preserved.
    """

    def __init__(
        self,
        height,
        width,
        interpolation="bilinear",
        crop_to_aspect_ratio=False,
        **kwargs,
    ):
        self.height = height
        self.width = width
        self.interpolation = interpolation
        self.crop_to_aspect_ratio = crop_to_aspect_ratio
        self._interpolation_method = image_utils.get_interpolation(
            interpolation
        )
        super().__init__(**kwargs)
        base_preprocessing_layer.keras_kpl_gauge.get_cell("Resizing").set(True)

    def call(self, inputs):
        # tf.image.resize will always output float32 and operate more
        # efficiently on float32 unless interpolation is nearest, in which case
        # ouput type matches input type.
        if self.interpolation == "nearest":
            input_dtype = self.compute_dtype
        else:
            input_dtype = tf.float32
        inputs = utils.ensure_tensor(inputs, dtype=input_dtype)
        size = [self.height, self.width]
        if self.crop_to_aspect_ratio:

            def resize_to_aspect(x):
                if tf_utils.is_ragged(inputs):
                    x = x.to_tensor()
                return image_utils.smart_resize(
                    x, size=size, interpolation=self._interpolation_method
                )

            if tf_utils.is_ragged(inputs):
                size_as_shape = tf.TensorShape(size)
                shape = size_as_shape + inputs.shape[-1:]
                spec = tf.TensorSpec(shape, input_dtype)
                outputs = tf.map_fn(
                    resize_to_aspect, inputs, fn_output_signature=spec
                )
            else:
                outputs = resize_to_aspect(inputs)
        else:
            outputs = tf.image.resize(
                inputs, size=size, method=self._interpolation_method
            )
        return tf.cast(outputs, self.compute_dtype)

    def compute_output_shape(self, input_shape):
        input_shape = tf.TensorShape(input_shape).as_list()
        input_shape[H_AXIS] = self.height
        input_shape[W_AXIS] = self.width
        return tf.TensorShape(input_shape)

    def get_config(self):
        config = {
            "height": self.height,
            "width": self.width,
            "interpolation": self.interpolation,
            "crop_to_aspect_ratio": self.crop_to_aspect_ratio,
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))


@keras_export(
    "keras.layers.CenterCrop",
    "keras.layers.experimental.preprocessing.CenterCrop",
)
class CenterCrop(base_layer.Layer):
    """A preprocessing layer which crops images.

    This layers crops the central portion of the images to a target size. If an
    image is smaller than the target size, it will be resized and cropped so as
    to return the largest possible window in the image that matches the target
    aspect ratio.

    Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and
    of interger or floating point dtype. By default, the layer will output
    floats.

    For an overview and full list of preprocessing layers, see the preprocessing
    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).

    Input shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`, in `"channels_last"` format.

    Output shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., target_height, target_width, channels)`.

    If the input height/width is even and the target height/width is odd (or
    inversely), the input image is left-padded by 1 pixel.

    Args:
      height: Integer, the height of the output shape.
      width: Integer, the width of the output shape.
    """

    def __init__(self, height, width, **kwargs):
        self.height = height
        self.width = width
        super().__init__(**kwargs, autocast=False)
        base_preprocessing_layer.keras_kpl_gauge.get_cell("CenterCrop").set(
            True
        )

    def call(self, inputs):
        inputs = utils.ensure_tensor(inputs, self.compute_dtype)
        input_shape = tf.shape(inputs)
        h_diff = input_shape[H_AXIS] - self.height
        w_diff = input_shape[W_AXIS] - self.width

        def center_crop():
            h_start = tf.cast(h_diff / 2, tf.int32)
            w_start = tf.cast(w_diff / 2, tf.int32)
            return tf.image.crop_to_bounding_box(
                inputs, h_start, w_start, self.height, self.width
            )

        def upsize():
            outputs = image_utils.smart_resize(
                inputs, [self.height, self.width]
            )
            # smart_resize will always output float32, so we need to re-cast.
            return tf.cast(outputs, self.compute_dtype)

        return tf.cond(
            tf.reduce_all((h_diff >= 0, w_diff >= 0)), center_crop, upsize
        )

    def compute_output_shape(self, input_shape):
        input_shape = tf.TensorShape(input_shape).as_list()
        input_shape[H_AXIS] = self.height
        input_shape[W_AXIS] = self.width
        return tf.TensorShape(input_shape)

    def get_config(self):
        config = {
            "height": self.height,
            "width": self.width,
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))


@keras_export("keras.__internal__.layers.BaseImageAugmentationLayer")
class BaseImageAugmentationLayer(base_layer.BaseRandomLayer):
    """Abstract base layer for image augmentation.

    This layer contains base functionalities for preprocessing layers which
    augment image related data, eg. image and in future, label and bounding
    boxes.  The subclasses could avoid making certain mistakes and reduce code
    duplications.

    This layer requires you to implement one method: `augment_image()`, which
    augments one single image during the training. There are a few additional
    methods that you can implement for added functionality on the layer:

    `augment_label()`, which handles label augmentation if the layer supports
    that.

    `augment_bounding_boxes()` is not implemented by this layer. Please use
    preprocessing layers in [KerasCV](https://keras.io/keras_cv/)
    for bounding box augmentation support.

    `get_random_transformation()`, which should produce a random transformation
    setting. The tranformation object, which could be any type, will be passed
    to `augment_image`, `augment_label` and `augment_bounding_boxes`, to
    coodinate the randomness behavior, eg, in the RandomFlip layer, the image
    and bounding_boxes should be changed in the same way.

    The `call()` method support two formats of inputs:
    1. Single image tensor with 3D (HWC) or 4D (NHWC) format.
    2. A dict of tensors with stable keys. The supported keys are:
      `"images"`, `"labels"` and `"bounding_boxes"` at the moment. We might add
      more keys in future when we support more types of augmentation.

    The output of the `call()` will be in two formats, which will be the same
    structure as the inputs.

    The `call()` will handle the logic detecting the training/inference mode,
    unpack the inputs, forward to the correct function, and pack the output back
    to the same structure as the inputs.

    By default the `call()` method leverages the `tf.vectorized_map()` function.
    Auto-vectorization can be disabled by setting `self.auto_vectorize = False`
    in your `__init__()` method.  When disabled, `call()` instead relies
    on `tf.map_fn()`. For example:

    ```python
    class SubclassLayer(BaseImageAugmentationLayer):
      def __init__(self):
        super().__init__()
        self.auto_vectorize = False
    ```

    Example:

    ```python
    class RandomContrast(BaseImageAugmentationLayer):

      def __init__(self, factor=(0.5, 1.5), **kwargs):
        super().__init__(**kwargs)
        self._factor = factor

      def augment_image(self, image, transformation):
        random_factor = tf.random.uniform([], self._factor[0], self._factor[1])
        mean = tf.math.reduced_mean(inputs, axis=-1, keep_dim=True)
        return (inputs - mean) * random_factor + mean
    ```

    Note that since the randomness is also a common functionnality, this layer
    also includes a tf.keras.backend.RandomGenerator, which can be used to
    produce the random numbers.  The random number generator is stored in the
    `self._random_generator` attribute.
    """

    def __init__(self, rate=1.0, seed=None, **kwargs):
        super().__init__(seed=seed, **kwargs)
        self.rate = rate

    @property
    def auto_vectorize(self):
        """Control whether automatic vectorization occurs.

        By default the `call()` method leverages the `tf.vectorized_map()`
        function.  Auto-vectorization can be disabled by setting
        `self.auto_vectorize = False` in your `__init__()` method.  When
        disabled, `call()` instead relies on `tf.map_fn()`. For example:

        ```python
        class SubclassLayer(BaseImageAugmentationLayer):
          def __init__(self):
            super().__init__()
            self.auto_vectorize = False
        ```
        """
        return getattr(self, "_auto_vectorize", True)

    @auto_vectorize.setter
    def auto_vectorize(self, auto_vectorize):
        self._auto_vectorize = auto_vectorize

    @property
    def _map_fn(self):
        if self.auto_vectorize:
            return tf.vectorized_map
        else:
            return tf.map_fn

    @doc_controls.for_subclass_implementers
    def augment_image(self, image, transformation):
        """Augment a single image during training.

        Args:
          image: 3D image input tensor to the layer. Forwarded from
            `layer.call()`.
          transformation: The transformation object produced by
            `get_random_transformation`. Used to coordinate the randomness
            between image, label and bounding box.

        Returns:
          output 3D tensor, which will be forward to `layer.call()`.
        """
        raise NotImplementedError()

    @doc_controls.for_subclass_implementers
    def augment_label(self, label, transformation):
        """Augment a single label during training.

        Args:
          label: 1D label to the layer. Forwarded from `layer.call()`.
          transformation: The transformation object produced by
            `get_random_transformation`. Used to coordinate the randomness
            between image, label and bounding box.

        Returns:
          output 1D tensor, which will be forward to `layer.call()`.
        """
        raise NotImplementedError()

    @doc_controls.for_subclass_implementers
    def augment_target(self, target, transformation):
        """Augment a single target during training.

        Args:
          target: 1D label to the layer. Forwarded from `layer.call()`.
          transformation: The transformation object produced by
            `get_random_transformation`. Used to coordinate the randomness
            between image, label and bounding box.

        Returns:
          output 1D tensor, which will be forward to `layer.call()`.
        """
        return self.augment_label(target, transformation)

    @doc_controls.for_subclass_implementers
    def augment_bounding_boxes(
        self, image, bounding_boxes, transformation=None
    ):
        """Augment bounding boxes for one image during training.

        Args:
          image: 3D image input tensor to the layer. Forwarded from
            `layer.call()`.
          bounding_boxes: 2D bounding boxes to the layer. Forwarded from
            `call()`.
          transformation: The transformation object produced by
            `get_random_transformation`. Used to coordinate the randomness
            between image, label and bounding box.

        Returns:
          output 2D tensor, which will be forward to `layer.call()`.
        """
        layer = self.__class__.__name__
        raise NotImplementedError(
            "In order to use bounding_boxes, "
            "please use "
            f"keras_cv.layers.{layer} "
            f"instead of keras.layers.{layer}."
        )

    @doc_controls.for_subclass_implementers
    def get_random_transformation(
        self, image=None, label=None, bounding_box=None
    ):
        """Produce random transformation config for one single input.

        This is used to produce same randomness between
        image/label/bounding_box.

        Args:
          image: 3D image tensor from inputs.
          label: optional 1D label tensor from inputs.
          bounding_box: optional 2D bounding boxes tensor from inputs.

        Returns:
          Any type of object, which will be forwarded to `augment_image`,
          `augment_label` and `augment_bounding_box` as the `transformation`
          parameter.
        """
        return None

    def call(self, inputs, training=True):
        inputs = self._ensure_inputs_are_compute_dtype(inputs)
        if training:
            inputs, is_dict, use_targets = self._format_inputs(inputs)
            images = inputs[IMAGES]
            if images.shape.rank == 3:
                return self._format_output(
                    self._augment(inputs), is_dict, use_targets
                )
            elif images.shape.rank == 4:
                return self._format_output(
                    self._batch_augment(inputs), is_dict, use_targets
                )
            else:
                raise ValueError(
                    "Image augmentation layers are expecting inputs to be "
                    "rank 3 (HWC) or 4D (NHWC) tensors. Got shape: "
                    f"{images.shape}"
                )
        else:
            return inputs

    def _augment(self, inputs):
        image = inputs.get(IMAGES, None)
        label = inputs.get(LABELS, None)
        bounding_box = inputs.get(BOUNDING_BOXES, None)
        transformation = self.get_random_transformation(
            image=image, label=label, bounding_box=bounding_box
        )
        image = self.augment_image(image, transformation=transformation)
        result = {IMAGES: image}
        if label is not None:
            label = self.augment_target(label, transformation=transformation)
            result[LABELS] = label
        if bounding_box is not None:
            bounding_box = self.augment_bounding_boxes(
                image, bounding_box, transformation=transformation
            )
            result[BOUNDING_BOXES] = bounding_box
        return result

    def _batch_augment(self, inputs):
        return self._map_fn(self._augment, inputs)

    def _format_inputs(self, inputs):
        if tf.is_tensor(inputs):
            # single image input tensor
            return {IMAGES: inputs}, False, False
        elif isinstance(inputs, dict) and TARGETS in inputs:
            # TODO(scottzhu): Check if it only contains the valid keys
            inputs[LABELS] = inputs[TARGETS]
            del inputs[TARGETS]
            return inputs, True, True
        elif isinstance(inputs, dict):
            return inputs, True, False
        else:
            raise ValueError(
                f"Expect the inputs to be image tensor or dict. Got {inputs}"
            )

    def _format_output(self, output, is_dict, use_targets):
        if not is_dict:
            return output[IMAGES]
        elif use_targets:
            output[TARGETS] = output[LABELS]
            del output[LABELS]
            return output
        else:
            return output

    def _ensure_inputs_are_compute_dtype(self, inputs):
        if isinstance(inputs, dict):
            inputs[IMAGES] = utils.ensure_tensor(
                inputs[IMAGES], self.compute_dtype
            )
        else:
            inputs = utils.ensure_tensor(inputs, self.compute_dtype)
        return inputs


@keras_export(
    "keras.layers.RandomCrop",
    "keras.layers.experimental.preprocessing.RandomCrop",
    v1=[],
)
class RandomCrop(BaseImageAugmentationLayer):
    """A preprocessing layer which randomly crops images during training.

    During training, this layer will randomly choose a location to crop images
    down to a target size. The layer will crop all the images in the same batch
    to the same cropping location.

    At inference time, and during training if an input image is smaller than the
    target size, the input will be resized and cropped so as to return the
    largest possible window in the image that matches the target aspect ratio.
    If you need to apply random cropping at inference time, set `training` to
    True when calling the layer.

    Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and
    of interger or floating point dtype. By default, the layer will output
    floats.

    For an overview and full list of preprocessing layers, see the preprocessing
    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).

    Input shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`, in `"channels_last"` format.

    Output shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., target_height, target_width, channels)`.

    Args:
      height: Integer, the height of the output shape.
      width: Integer, the width of the output shape.
      seed: Integer. Used to create a random seed.
    """

    def __init__(self, height, width, seed=None, **kwargs):
        base_preprocessing_layer.keras_kpl_gauge.get_cell("RandomCrop").set(
            True
        )
        super().__init__(
            **kwargs, autocast=False, seed=seed, force_generator=True
        )
        self.height = height
        self.width = width
        self.seed = seed

    def call(self, inputs, training=True):

        if training:
            return super().call(inputs, training)
        else:
            inputs = self._ensure_inputs_are_compute_dtype(inputs)
            inputs, is_dict, targets = self._format_inputs(inputs)
            output = inputs
            # self._resize() returns valid results for both batched and
            # unbatched
            output["images"] = self._resize(inputs["images"])
            return self._format_output(output, is_dict, targets)

    def get_random_transformation(
        self, image=None, label=None, bounding_box=None
    ):
        input_shape = tf.shape(image)
        h_diff = input_shape[H_AXIS] - self.height
        w_diff = input_shape[W_AXIS] - self.width
        dtype = input_shape.dtype
        rands = self._random_generator.random_uniform([2], 0, dtype.max, dtype)
        h_start = rands[0] % (h_diff + 1)
        w_start = rands[1] % (w_diff + 1)
        return {"top": h_start, "left": w_start}

    def augment_image(self, image, transformation):
        input_shape = tf.shape(image)
        h_diff = input_shape[H_AXIS] - self.height
        w_diff = input_shape[W_AXIS] - self.width
        return tf.cond(
            tf.reduce_all((h_diff >= 0, w_diff >= 0)),
            lambda: self._crop(image, transformation),
            lambda: self._resize(image),
        )

    def _crop(self, image, transformation):
        top = transformation["top"]
        left = transformation["left"]
        return tf.image.crop_to_bounding_box(
            image, top, left, self.height, self.width
        )

    def _resize(self, image):
        outputs = image_utils.smart_resize(image, [self.height, self.width])
        # smart_resize will always output float32, so we need to re-cast.
        return tf.cast(outputs, self.compute_dtype)

    def compute_output_shape(self, input_shape):
        input_shape = tf.TensorShape(input_shape).as_list()
        input_shape[H_AXIS] = self.height
        input_shape[W_AXIS] = self.width
        return tf.TensorShape(input_shape)

    def get_config(self):
        config = {
            "height": self.height,
            "width": self.width,
            "seed": self.seed,
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))


@keras_export(
    "keras.layers.Rescaling",
    "keras.layers.experimental.preprocessing.Rescaling",
)
class Rescaling(base_layer.Layer):
    """A preprocessing layer which rescales input values to a new range.

    This layer rescales every value of an input (often an image) by multiplying
    by `scale` and adding `offset`.

    For instance:

    1. To rescale an input in the ``[0, 255]`` range
    to be in the `[0, 1]` range, you would pass `scale=1./255`.

    2. To rescale an input in the ``[0, 255]`` range to be in the `[-1, 1]`
    range, you would pass `scale=1./127.5, offset=-1`.

    The rescaling is applied both during training and inference. Inputs can be
    of integer or floating point dtype, and by default the layer will output
    floats.

    For an overview and full list of preprocessing layers, see the preprocessing
    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).

    Input shape:
      Arbitrary.

    Output shape:
      Same as input.

    Args:
      scale: Float, the scale to apply to the inputs.
      offset: Float, the offset to apply to the inputs.
    """

    def __init__(self, scale, offset=0.0, **kwargs):
        self.scale = scale
        self.offset = offset
        super().__init__(**kwargs)
        base_preprocessing_layer.keras_kpl_gauge.get_cell("Rescaling").set(True)

    def call(self, inputs):
        dtype = self.compute_dtype
        scale = tf.cast(self.scale, dtype)
        offset = tf.cast(self.offset, dtype)
        return tf.cast(inputs, dtype) * scale + offset

    def compute_output_shape(self, input_shape):
        return input_shape

    def get_config(self):
        config = {
            "scale": self.scale,
            "offset": self.offset,
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))


HORIZONTAL = "horizontal"
VERTICAL = "vertical"
HORIZONTAL_AND_VERTICAL = "horizontal_and_vertical"


@keras_export(
    "keras.layers.RandomFlip",
    "keras.layers.experimental.preprocessing.RandomFlip",
    v1=[],
)
class RandomFlip(BaseImageAugmentationLayer):
    """A preprocessing layer which randomly flips images during training.

    This layer will flip the images horizontally and or vertically based on the
    `mode` attribute. During inference time, the output will be identical to
    input. Call the layer with `training=True` to flip the input.

    Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and
    of interger or floating point dtype. By default, the layer will output
    floats.

    For an overview and full list of preprocessing layers, see the preprocessing
    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).

    Input shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`, in `"channels_last"` format.

    Output shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`, in `"channels_last"` format.

    Arguments:
      mode: String indicating which flip mode to use. Can be `"horizontal"`,
        `"vertical"`, or `"horizontal_and_vertical"`. Defaults to
        `"horizontal_and_vertical"`. `"horizontal"` is a left-right flip and
        `"vertical"` is a top-bottom flip.
      seed: Integer. Used to create a random seed.
    """

    def __init__(self, mode=HORIZONTAL_AND_VERTICAL, seed=None, **kwargs):
        super().__init__(seed=seed, force_generator=True, **kwargs)
        base_preprocessing_layer.keras_kpl_gauge.get_cell("RandomFlip").set(
            True
        )
        self.mode = mode
        if mode == HORIZONTAL:
            self.horizontal = True
            self.vertical = False
        elif mode == VERTICAL:
            self.horizontal = False
            self.vertical = True
        elif mode == HORIZONTAL_AND_VERTICAL:
            self.horizontal = True
            self.vertical = True
        else:
            raise ValueError(
                "RandomFlip layer {name} received an unknown mode "
                "argument {arg}".format(name=self.name, arg=mode)
            )
        self.auto_vectorize = False

    def augment_label(self, label, transformation):
        return label

    def augment_image(self, image, transformation):
        flipped_outputs = image
        if self.horizontal and transformation["flip_horizontal"]:
            flipped_outputs = tf.image.flip_left_right(flipped_outputs)
        if self.vertical and transformation["flip_vertical"]:
            flipped_outputs = tf.image.flip_up_down(flipped_outputs)
        flipped_outputs.set_shape(image.shape)
        return flipped_outputs

    def get_random_transformation(
        self, image=None, label=None, bounding_box=None
    ):
        flip_horizontal = False
        flip_vertical = False
        if self.horizontal:
            flip_horizontal = np.random.choice([True, False])
        if self.vertical:
            flip_vertical = np.random.choice([True, False])
        return {
            "flip_horizontal": flip_horizontal,
            "flip_vertical": flip_vertical,
        }

    def compute_output_shape(self, input_shape):
        return input_shape

    def get_config(self):
        config = {
            "mode": self.mode,
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))


# TODO(tanzheny): Add examples, here and everywhere.
@keras_export(
    "keras.layers.RandomTranslation",
    "keras.layers.experimental.preprocessing.RandomTranslation",
    v1=[],
)
class RandomTranslation(BaseImageAugmentationLayer):
    """A preprocessing layer which randomly translates images during training.

    This layer will apply random translations to each image during training,
    filling empty space according to `fill_mode`.

    Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and
    of interger or floating point dtype. By default, the layer will output
    floats.

    For an overview and full list of preprocessing layers, see the preprocessing
    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).

    Args:
      height_factor: a float represented as fraction of value, or a tuple of
        size 2 representing lower and upper bound for shifting vertically. A
        negative value means shifting image up, while a positive value means
        shifting image down. When represented as a single positive float, this
        value is used for both the upper and lower bound. For instance,
        `height_factor=(-0.2, 0.3)` results in an output shifted by a random
        amount in the range `[-20%, +30%]`.  `height_factor=0.2` results in an
        output height shifted by a random amount in the range `[-20%, +20%]`.
      width_factor: a float represented as fraction of value, or a tuple of size
        2 representing lower and upper bound for shifting horizontally. A
        negative value means shifting image left, while a positive value means
        shifting image right. When represented as a single positive float, this
        value is used for both the upper and lower bound. For instance,
        `width_factor=(-0.2, 0.3)` results in an output shifted left by 20%, and
        shifted right by 30%. `width_factor=0.2` results in an output height
        shifted left or right by 20%.
      fill_mode: Points outside the boundaries of the input are filled according
        to the given mode (one of `{"constant", "reflect", "wrap", "nearest"}`).
        - *reflect*: `(d c b a | a b c d | d c b a)` The input is extended by
          reflecting about the edge of the last pixel.
        - *constant*: `(k k k k | a b c d | k k k k)` The input is extended by
          filling all values beyond the edge with the same constant value k = 0.
        - *wrap*: `(a b c d | a b c d | a b c d)` The input is extended by
          wrapping around to the opposite edge.
        - *nearest*: `(a a a a | a b c d | d d d d)` The input is extended by
          the nearest pixel.
      interpolation: Interpolation mode. Supported values: `"nearest"`,
        `"bilinear"`.
      seed: Integer. Used to create a random seed.
      fill_value: a float represents the value to be filled outside the
        boundaries when `fill_mode="constant"`.

    Input shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`,  in `"channels_last"` format.

    Output shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`,  in `"channels_last"` format.
    """

    def __init__(
        self,
        height_factor,
        width_factor,
        fill_mode="reflect",
        interpolation="bilinear",
        seed=None,
        fill_value=0.0,
        **kwargs,
    ):
        base_preprocessing_layer.keras_kpl_gauge.get_cell(
            "RandomTranslation"
        ).set(True)
        super().__init__(seed=seed, force_generator=True, **kwargs)
        self.height_factor = height_factor
        if isinstance(height_factor, (tuple, list)):
            self.height_lower = height_factor[0]
            self.height_upper = height_factor[1]
        else:
            self.height_lower = -height_factor
            self.height_upper = height_factor
        if self.height_upper < self.height_lower:
            raise ValueError(
                "`height_factor` cannot have upper bound less than "
                "lower bound, got {}".format(height_factor)
            )
        if abs(self.height_lower) > 1.0 or abs(self.height_upper) > 1.0:
            raise ValueError(
                "`height_factor` must have values between [-1, 1], "
                "got {}".format(height_factor)
            )

        self.width_factor = width_factor
        if isinstance(width_factor, (tuple, list)):
            self.width_lower = width_factor[0]
            self.width_upper = width_factor[1]
        else:
            self.width_lower = -width_factor
            self.width_upper = width_factor
        if self.width_upper < self.width_lower:
            raise ValueError(
                "`width_factor` cannot have upper bound less than "
                "lower bound, got {}".format(width_factor)
            )
        if abs(self.width_lower) > 1.0 or abs(self.width_upper) > 1.0:
            raise ValueError(
                "`width_factor` must have values between [-1, 1], "
                "got {}".format(width_factor)
            )

        check_fill_mode_and_interpolation(fill_mode, interpolation)

        self.fill_mode = fill_mode
        self.fill_value = fill_value
        self.interpolation = interpolation
        self.seed = seed

    @tf.function
    def augment_image(self, image, transformation):
        """Translated inputs with random ops."""
        # The transform op only accepts rank 4 inputs, so if we have an
        # unbatched image, we need to temporarily expand dims to a batch.
        original_shape = image.shape
        inputs = tf.expand_dims(image, 0)

        inputs_shape = tf.shape(inputs)
        img_hd = tf.cast(inputs_shape[H_AXIS], tf.float32)
        img_wd = tf.cast(inputs_shape[W_AXIS], tf.float32)
        height_translation = transformation["height_translation"]
        width_translation = transformation["width_translation"]
        height_translation = height_translation * img_hd
        width_translation = width_translation * img_wd
        translations = tf.cast(
            tf.concat([width_translation, height_translation], axis=1),
            dtype=tf.float32,
        )
        output = transform(
            inputs,
            get_translation_matrix(translations),
            interpolation=self.interpolation,
            fill_mode=self.fill_mode,
            fill_value=self.fill_value,
        )

        output = tf.squeeze(output, 0)
        output.set_shape(original_shape)
        return output

    def get_random_transformation(
        self, image=None, label=None, bounding_box=None
    ):
        del image, label, bounding_box
        batch_size = 1
        height_translation = self._random_generator.random_uniform(
            shape=[batch_size, 1],
            minval=self.height_lower,
            maxval=self.height_upper,
            dtype=tf.float32,
        )
        width_translation = self._random_generator.random_uniform(
            shape=[batch_size, 1],
            minval=self.width_lower,
            maxval=self.width_upper,
            dtype=tf.float32,
        )
        return {
            "height_translation": height_translation,
            "width_translation": width_translation,
        }

    def _batch_augment(self, inputs):
        # Change to vectorized_map for better performance, as well as work
        # around issue for different tensorspec between inputs and outputs.
        return tf.vectorized_map(self._augment, inputs)

    def augment_label(self, label, transformation):
        return label

    def compute_output_shape(self, input_shape):
        return input_shape

    def get_config(self):
        config = {
            "height_factor": self.height_factor,
            "width_factor": self.width_factor,
            "fill_mode": self.fill_mode,
            "fill_value": self.fill_value,
            "interpolation": self.interpolation,
            "seed": self.seed,
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))


def get_translation_matrix(translations, name=None):
    """Returns projective transform(s) for the given translation(s).

    Args:
      translations: A matrix of 2-element lists representing `[dx, dy]`
        to translate for each image (for a batch of images).
      name: The name of the op.

    Returns:
      A tensor of shape `(num_images, 8)` projective transforms which can be
        given to `transform`.
    """
    with backend.name_scope(name or "translation_matrix"):
        num_translations = tf.shape(translations)[0]
        # The translation matrix looks like:
        #     [[1 0 -dx]
        #      [0 1 -dy]
        #      [0 0 1]]
        # where the last entry is implicit.
        # Translation matrices are always float32.
        return tf.concat(
            values=[
                tf.ones((num_translations, 1), tf.float32),
                tf.zeros((num_translations, 1), tf.float32),
                -translations[:, 0, None],
                tf.zeros((num_translations, 1), tf.float32),
                tf.ones((num_translations, 1), tf.float32),
                -translations[:, 1, None],
                tf.zeros((num_translations, 2), tf.float32),
            ],
            axis=1,
        )


def transform(
    images,
    transforms,
    fill_mode="reflect",
    fill_value=0.0,
    interpolation="bilinear",
    output_shape=None,
    name=None,
):
    """Applies the given transform(s) to the image(s).

    Args:
      images: A tensor of shape
        `(num_images, num_rows, num_columns, num_channels)` (NHWC). The rank
        must be statically known (the shape is not `TensorShape(None)`).
      transforms: Projective transform matrix/matrices. A vector of length 8 or
        tensor of size N x 8. If one row of transforms is [a0, a1, a2, b0, b1,
        b2, c0, c1], then it maps the *output* point `(x, y)` to a transformed
        *input* point
        `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`,
        where `k = c0 x + c1 y + 1`. The transforms are *inverted* compared
        to the transform mapping input points to output points. Note that
        gradients are not backpropagated into transformation parameters.
      fill_mode: Points outside the boundaries of the input are filled according
        to the given mode (one of `{"constant", "reflect", "wrap", "nearest"}`).
      fill_value: a float represents the value to be filled outside the
        boundaries when `fill_mode="constant"`.
      interpolation: Interpolation mode. Supported values: `"nearest"`,
        `"bilinear"`.
      output_shape: Output dimension after the transform, `[height, width]`.
        If `None`, output is the same size as input image.
      name: The name of the op.

    Fill mode behavior for each valid value is as follows:

    - reflect (d c b a | a b c d | d c b a)
    The input is extended by reflecting about the edge of the last pixel.

    - constant (k k k k | a b c d | k k k k)
    The input is extended by filling all
    values beyond the edge with the same constant value k = 0.

    - wrap (a b c d | a b c d | a b c d)
    The input is extended by wrapping around to the opposite edge.

    - nearest (a a a a | a b c d | d d d d)
    The input is extended by the nearest pixel.

    Input shape:
      4D tensor with shape: `(samples, height, width, channels)`,
        in `"channels_last"` format.

    Output shape:
      4D tensor with shape: `(samples, height, width, channels)`,
        in `"channels_last"` format.

    Returns:
      Image(s) with the same type and shape as `images`, with the given
      transform(s) applied. Transformed coordinates outside of the input image
      will be filled with zeros.

    Raises:
      TypeError: If `image` is an invalid type.
      ValueError: If output shape is not 1-D int32 Tensor.
    """
    with backend.name_scope(name or "transform"):
        if output_shape is None:
            output_shape = tf.shape(images)[1:3]
            if not tf.executing_eagerly():
                output_shape_value = tf.get_static_value(output_shape)
                if output_shape_value is not None:
                    output_shape = output_shape_value

        output_shape = tf.convert_to_tensor(
            output_shape, tf.int32, name="output_shape"
        )

        if not output_shape.get_shape().is_compatible_with([2]):
            raise ValueError(
                "output_shape must be a 1-D Tensor of 2 elements: "
                "new_height, new_width, instead got "
                "{}".format(output_shape)
            )

        fill_value = tf.convert_to_tensor(
            fill_value, tf.float32, name="fill_value"
        )

        return tf.raw_ops.ImageProjectiveTransformV3(
            images=images,
            output_shape=output_shape,
            fill_value=fill_value,
            transforms=transforms,
            fill_mode=fill_mode.upper(),
            interpolation=interpolation.upper(),
        )


def get_rotation_matrix(angles, image_height, image_width, name=None):
    """Returns projective transform(s) for the given angle(s).

    Args:
      angles: A scalar angle to rotate all images by, or (for batches of images)
        a vector with an angle to rotate each image in the batch. The rank must
        be statically known (the shape is not `TensorShape(None)`).
      image_height: Height of the image(s) to be transformed.
      image_width: Width of the image(s) to be transformed.
      name: The name of the op.

    Returns:
      A tensor of shape (num_images, 8). Projective transforms which can be
        given to operation `image_projective_transform_v2`. If one row of
        transforms is [a0, a1, a2, b0, b1, b2, c0, c1], then it maps the
        *output* point `(x, y)` to a transformed *input* point
        `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`,
        where `k = c0 x + c1 y + 1`.
    """
    with backend.name_scope(name or "rotation_matrix"):
        x_offset = (
            (image_width - 1)
            - (
                tf.cos(angles) * (image_width - 1)
                - tf.sin(angles) * (image_height - 1)
            )
        ) / 2.0
        y_offset = (
            (image_height - 1)
            - (
                tf.sin(angles) * (image_width - 1)
                + tf.cos(angles) * (image_height - 1)
            )
        ) / 2.0
        num_angles = tf.shape(angles)[0]
        return tf.concat(
            values=[
                tf.cos(angles)[:, None],
                -tf.sin(angles)[:, None],
                x_offset[:, None],
                tf.sin(angles)[:, None],
                tf.cos(angles)[:, None],
                y_offset[:, None],
                tf.zeros((num_angles, 2), tf.float32),
            ],
            axis=1,
        )


@keras_export(
    "keras.layers.RandomRotation",
    "keras.layers.experimental.preprocessing.RandomRotation",
    v1=[],
)
class RandomRotation(BaseImageAugmentationLayer):
    """A preprocessing layer which randomly rotates images during training.

    This layer will apply random rotations to each image, filling empty space
    according to `fill_mode`.

    By default, random rotations are only applied during training.
    At inference time, the layer does nothing. If you need to apply random
    rotations at inference time, set `training` to True when calling the layer.

    Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and
    of interger or floating point dtype. By default, the layer will output
    floats.

    For an overview and full list of preprocessing layers, see the preprocessing
    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).

    Input shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`, in `"channels_last"` format

    Output shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`, in `"channels_last"` format

    Arguments:
      factor: a float represented as fraction of 2 Pi, or a tuple of size 2
        representing lower and upper bound for rotating clockwise and
        counter-clockwise. A positive values means rotating counter clock-wise,
        while a negative value means clock-wise. When represented as a single
        float, this value is used for both the upper and lower bound. For
        instance, `factor=(-0.2, 0.3)` results in an output rotation by a random
        amount in the range `[-20% * 2pi, 30% * 2pi]`. `factor=0.2` results in
        an output rotating by a random amount in the range
        `[-20% * 2pi, 20% * 2pi]`.
      fill_mode: Points outside the boundaries of the input are filled according
        to the given mode (one of `{"constant", "reflect", "wrap", "nearest"}`).
        - *reflect*: `(d c b a | a b c d | d c b a)` The input is extended by
          reflecting about the edge of the last pixel.
        - *constant*: `(k k k k | a b c d | k k k k)` The input is extended by
          filling all values beyond the edge with the same constant value k = 0.
        - *wrap*: `(a b c d | a b c d | a b c d)` The input is extended by
          wrapping around to the opposite edge.
        - *nearest*: `(a a a a | a b c d | d d d d)` The input is extended by
          the nearest pixel.
      interpolation: Interpolation mode. Supported values: `"nearest"`,
        `"bilinear"`.
      seed: Integer. Used to create a random seed.
      fill_value: a float represents the value to be filled outside the
        boundaries when `fill_mode="constant"`.
    """

    def __init__(
        self,
        factor,
        fill_mode="reflect",
        interpolation="bilinear",
        seed=None,
        fill_value=0.0,
        **kwargs,
    ):
        base_preprocessing_layer.keras_kpl_gauge.get_cell("RandomRotation").set(
            True
        )
        super().__init__(seed=seed, force_generator=True, **kwargs)
        self.factor = factor
        if isinstance(factor, (tuple, list)):
            self.lower = factor[0]
            self.upper = factor[1]
        else:
            self.lower = -factor
            self.upper = factor
        if self.upper < self.lower:
            raise ValueError(
                f"Factor cannot have negative values, got {factor}"
            )
        check_fill_mode_and_interpolation(fill_mode, interpolation)
        self.fill_mode = fill_mode
        self.fill_value = fill_value
        self.interpolation = interpolation
        self.seed = seed

    def get_random_transformation(
        self, image=None, label=None, bounding_box=None
    ):
        min_angle = self.lower * 2.0 * np.pi
        max_angle = self.upper * 2.0 * np.pi
        angle = self._random_generator.random_uniform(
            shape=[1], minval=min_angle, maxval=max_angle
        )
        return {"angle": angle}

    def augment_image(self, image, transformation):
        image = utils.ensure_tensor(image, self.compute_dtype)
        original_shape = image.shape
        image = tf.expand_dims(image, 0)
        image_shape = tf.shape(image)
        img_hd = tf.cast(image_shape[H_AXIS], tf.float32)
        img_wd = tf.cast(image_shape[W_AXIS], tf.float32)
        angle = transformation["angle"]
        output = transform(
            image,
            get_rotation_matrix(angle, img_hd, img_wd),
            fill_mode=self.fill_mode,
            fill_value=self.fill_value,
            interpolation=self.interpolation,
        )
        output = tf.squeeze(output, 0)
        output.set_shape(original_shape)
        return output

    def augment_label(self, label, transformation):
        return label

    def compute_output_shape(self, input_shape):
        return input_shape

    def get_config(self):
        config = {
            "factor": self.factor,
            "fill_mode": self.fill_mode,
            "fill_value": self.fill_value,
            "interpolation": self.interpolation,
            "seed": self.seed,
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))


@keras_export(
    "keras.layers.RandomZoom",
    "keras.layers.experimental.preprocessing.RandomZoom",
    v1=[],
)
class RandomZoom(BaseImageAugmentationLayer):
    """A preprocessing layer which randomly zooms images during training.

    This layer will randomly zoom in or out on each axis of an image
    independently, filling empty space according to `fill_mode`.

    Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and
    of interger or floating point dtype. By default, the layer will output
    floats.

    For an overview and full list of preprocessing layers, see the preprocessing
    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).

    Args:
      height_factor: a float represented as fraction of value, or a tuple of
        size 2 representing lower and upper bound for zooming vertically. When
        represented as a single float, this value is used for both the upper and
        lower bound. A positive value means zooming out, while a negative value
        means zooming in. For instance, `height_factor=(0.2, 0.3)` result in an
        output zoomed out by a random amount in the range `[+20%, +30%]`.
        `height_factor=(-0.3, -0.2)` result in an output zoomed in by a random
        amount in the range `[+20%, +30%]`.
      width_factor: a float represented as fraction of value, or a tuple of size
        2 representing lower and upper bound for zooming horizontally. When
        represented as a single float, this value is used for both the upper and
        lower bound. For instance, `width_factor=(0.2, 0.3)` result in an output
        zooming out between 20% to 30%. `width_factor=(-0.3, -0.2)` result in an
        output zooming in between 20% to 30%. Defaults to `None`, i.e., zooming
        vertical and horizontal directions by preserving the aspect ratio.
      fill_mode: Points outside the boundaries of the input are filled according
        to the given mode (one of `{"constant", "reflect", "wrap", "nearest"}`).
        - *reflect*: `(d c b a | a b c d | d c b a)` The input is extended by
          reflecting about the edge of the last pixel.
        - *constant*: `(k k k k | a b c d | k k k k)` The input is extended by
          filling all values beyond the edge with the same constant value k = 0.
        - *wrap*: `(a b c d | a b c d | a b c d)` The input is extended by
          wrapping around to the opposite edge.
        - *nearest*: `(a a a a | a b c d | d d d d)` The input is extended by
          the nearest pixel.
      interpolation: Interpolation mode. Supported values: `"nearest"`,
        `"bilinear"`.
      seed: Integer. Used to create a random seed.
      fill_value: a float represents the value to be filled outside the
        boundaries when `fill_mode="constant"`.

    Example:

    >>> input_img = np.random.random((32, 224, 224, 3))
    >>> layer = tf.keras.layers.RandomZoom(.5, .2)
    >>> out_img = layer(input_img)
    >>> out_img.shape
    TensorShape([32, 224, 224, 3])

    Input shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`, in `"channels_last"` format.

    Output shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`, in `"channels_last"` format.
    """

    def __init__(
        self,
        height_factor,
        width_factor=None,
        fill_mode="reflect",
        interpolation="bilinear",
        seed=None,
        fill_value=0.0,
        **kwargs,
    ):
        base_preprocessing_layer.keras_kpl_gauge.get_cell("RandomZoom").set(
            True
        )
        super().__init__(seed=seed, force_generator=True, **kwargs)
        self.height_factor = height_factor
        if isinstance(height_factor, (tuple, list)):
            self.height_lower = height_factor[0]
            self.height_upper = height_factor[1]
        else:
            self.height_lower = -height_factor
            self.height_upper = height_factor

        if abs(self.height_lower) > 1.0 or abs(self.height_upper) > 1.0:
            raise ValueError(
                "`height_factor` must have values between [-1, 1], "
                "got {}".format(height_factor)
            )

        self.width_factor = width_factor
        if width_factor is not None:
            if isinstance(width_factor, (tuple, list)):
                self.width_lower = width_factor[0]
                self.width_upper = width_factor[1]
            else:
                self.width_lower = -width_factor
                self.width_upper = width_factor

            if self.width_lower < -1.0 or self.width_upper < -1.0:
                raise ValueError(
                    "`width_factor` must have values larger than -1, "
                    "got {}".format(width_factor)
                )

        check_fill_mode_and_interpolation(fill_mode, interpolation)

        self.fill_mode = fill_mode
        self.fill_value = fill_value
        self.interpolation = interpolation
        self.seed = seed

    def get_random_transformation(
        self, image=None, label=None, bounding_box=None
    ):
        height_zoom = self._random_generator.random_uniform(
            shape=[1, 1],
            minval=1.0 + self.height_lower,
            maxval=1.0 + self.height_upper,
        )
        if self.width_factor is not None:
            width_zoom = self._random_generator.random_uniform(
                shape=[1, 1],
                minval=1.0 + self.width_lower,
                maxval=1.0 + self.width_upper,
            )
        else:
            width_zoom = height_zoom

        return {"height_zoom": height_zoom, "width_zoom": width_zoom}

    def augment_image(self, image, transformation):
        image = utils.ensure_tensor(image, self.compute_dtype)
        original_shape = image.shape
        image = tf.expand_dims(image, 0)
        image_shape = tf.shape(image)
        img_hd = tf.cast(image_shape[H_AXIS], tf.float32)
        img_wd = tf.cast(image_shape[W_AXIS], tf.float32)
        width_zoom = transformation["width_zoom"]
        height_zoom = transformation["height_zoom"]
        zooms = tf.cast(
            tf.concat([width_zoom, height_zoom], axis=1), dtype=tf.float32
        )
        output = transform(
            image,
            get_zoom_matrix(zooms, img_hd, img_wd),
            fill_mode=self.fill_mode,
            fill_value=self.fill_value,
            interpolation=self.interpolation,
        )
        output = tf.squeeze(output, 0)
        output.set_shape(original_shape)
        return output

    def augment_label(self, label, transformation):
        return label

    def compute_output_shape(self, input_shape):
        return input_shape

    def get_config(self):
        config = {
            "height_factor": self.height_factor,
            "width_factor": self.width_factor,
            "fill_mode": self.fill_mode,
            "fill_value": self.fill_value,
            "interpolation": self.interpolation,
            "seed": self.seed,
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))


def get_zoom_matrix(zooms, image_height, image_width, name=None):
    """Returns projective transform(s) for the given zoom(s).

    Args:
      zooms: A matrix of 2-element lists representing `[zx, zy]` to zoom for
        each image (for a batch of images).
      image_height: Height of the image(s) to be transformed.
      image_width: Width of the image(s) to be transformed.
      name: The name of the op.

    Returns:
      A tensor of shape `(num_images, 8)`. Projective transforms which can be
        given to operation `image_projective_transform_v2`.
        If one row of transforms is
         `[a0, a1, a2, b0, b1, b2, c0, c1]`, then it maps the *output* point
         `(x, y)` to a transformed *input* point
         `(x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k)`,
         where `k = c0 x + c1 y + 1`.
    """
    with backend.name_scope(name or "zoom_matrix"):
        num_zooms = tf.shape(zooms)[0]
        # The zoom matrix looks like:
        #     [[zx 0 0]
        #      [0 zy 0]
        #      [0 0 1]]
        # where the last entry is implicit.
        # Zoom matrices are always float32.
        x_offset = ((image_width - 1.0) / 2.0) * (1.0 - zooms[:, 0, None])
        y_offset = ((image_height - 1.0) / 2.0) * (1.0 - zooms[:, 1, None])
        return tf.concat(
            values=[
                zooms[:, 0, None],
                tf.zeros((num_zooms, 1), tf.float32),
                x_offset,
                tf.zeros((num_zooms, 1), tf.float32),
                zooms[:, 1, None],
                y_offset,
                tf.zeros((num_zooms, 2), tf.float32),
            ],
            axis=1,
        )


@keras_export(
    "keras.layers.RandomContrast",
    "keras.layers.experimental.preprocessing.RandomContrast",
    v1=[],
)
class RandomContrast(BaseImageAugmentationLayer):
    """A preprocessing layer which randomly adjusts contrast during training.

    This layer will randomly adjust the contrast of an image or images by a
    random factor. Contrast is adjusted independently for each channel of each
    image during training.

    For each channel, this layer computes the mean of the image pixels in the
    channel and then adjusts each component `x` of each pixel to
    `(x - mean) * contrast_factor + mean`.

    Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and
    in integer or floating point dtype. By default, the layer will output
    floats. The output value will be clipped to the range `[0, 255]`, the valid
    range of RGB colors.

    For an overview and full list of preprocessing layers, see the preprocessing
    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).

    Input shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`, in `"channels_last"` format.

    Output shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`, in `"channels_last"` format.

    Arguments:
      factor: a positive float represented as fraction of value, or a tuple of
        size 2 representing lower and upper bound. When represented as a single
        float, lower = upper. The contrast factor will be randomly picked
        between `[1.0 - lower, 1.0 + upper]`. For any pixel x in the channel,
        the output will be `(x - mean) * factor + mean` where `mean` is the mean
        value of the channel.
      seed: Integer. Used to create a random seed.
    """

    def __init__(self, factor, seed=None, **kwargs):
        base_preprocessing_layer.keras_kpl_gauge.get_cell("RandomContrast").set(
            True
        )
        super().__init__(seed=seed, force_generator=True, **kwargs)
        self.factor = factor
        if isinstance(factor, (tuple, list)):
            self.lower = factor[0]
            self.upper = factor[1]
        else:
            self.lower = self.upper = factor
        if self.lower < 0.0 or self.upper < 0.0 or self.lower > 1.0:
            raise ValueError(
                "Factor cannot have negative values or greater than 1.0,"
                " got {}".format(factor)
            )
        self.seed = seed

    def get_random_transformation(
        self, image=None, label=None, bounding_box=None
    ):
        lower = 1.0 - self.lower
        upper = 1.0 + self.upper
        random_seed = self._random_generator.make_seed_for_stateless_op()
        contrast_factor = stateless_random_ops.stateless_random_uniform(
            shape=[], minval=lower, maxval=upper, seed=random_seed
        )
        return {"contrast_factor": contrast_factor}

    def augment_image(self, image, transformation):
        contrast_factor = transformation["contrast_factor"]
        output = tf.image.adjust_contrast(
            image, contrast_factor=contrast_factor
        )
        output = tf.clip_by_value(output, 0, 255)
        output.set_shape(image.shape)
        return output

    def augment_label(self, label, transformation):
        return label

    def compute_output_shape(self, input_shape):
        return input_shape

    def get_config(self):
        config = {
            "factor": self.factor,
            "seed": self.seed,
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))


@keras_export("keras.layers.RandomBrightness", v1=[])
class RandomBrightness(BaseImageAugmentationLayer):
    """A preprocessing layer which randomly adjusts brightness during training.

    This layer will randomly increase/reduce the brightness for the input RGB
    images. At inference time, the output will be identical to the input.
    Call the layer with `training=True` to adjust the brightness of the input.

    Note that different brightness adjustment factors
    will be apply to each the images in the batch.

    For an overview and full list of preprocessing layers, see the preprocessing
    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).

    Args:
      factor: Float or a list/tuple of 2 floats between -1.0 and 1.0. The
        factor is used to determine the lower bound and upper bound of the
        brightness adjustment. A float value will be chosen randomly between
        the limits. When -1.0 is chosen, the output image will be black, and
        when 1.0 is chosen, the image will be fully white. When only one float
        is provided, eg, 0.2, then -0.2 will be used for lower bound and 0.2
        will be used for upper bound.
      value_range: Optional list/tuple of 2 floats for the lower and upper limit
        of the values of the input data. Defaults to [0.0, 255.0]. Can be
        changed to e.g. [0.0, 1.0] if the image input has been scaled before
        this layer.  The brightness adjustment will be scaled to this range, and
        the output values will be clipped to this range.
      seed: optional integer, for fixed RNG behavior.

    Inputs: 3D (HWC) or 4D (NHWC) tensor, with float or int dtype. Input pixel
      values can be of any range (e.g. `[0., 1.)` or `[0, 255]`)

    Output: 3D (HWC) or 4D (NHWC) tensor with brightness adjusted based on the
      `factor`. By default, the layer will output floats. The output value will
      be clipped to the range `[0, 255]`, the valid range of RGB colors, and
      rescaled based on the `value_range` if needed.

    Sample usage:

    ```python
    random_bright = tf.keras.layers.RandomBrightness(factor=0.2)

    # An image with shape [2, 2, 3]
    image = [[[1, 2, 3], [4 ,5 ,6]], [[7, 8, 9], [10, 11, 12]]]

    # Assume we randomly select the factor to be 0.1, then it will apply
    # 0.1 * 255 to all the channel
    output = random_bright(image, training=True)

    # output will be int64 with 25.5 added to each channel and round down.
    tf.Tensor([[[26.5, 27.5, 28.5]
                [29.5, 30.5, 31.5]]
               [[32.5, 33.5, 34.5]
                [35.5, 36.5, 37.5]]],
              shape=(2, 2, 3), dtype=int64)
    ```
    """

    _FACTOR_VALIDATION_ERROR = (
        "The `factor` argument should be a number (or a list of two numbers) "
        "in the range [-1.0, 1.0]. "
    )
    _VALUE_RANGE_VALIDATION_ERROR = (
        "The `value_range` argument should be a list of two numbers. "
    )

    def __init__(self, factor, value_range=(0, 255), seed=None, **kwargs):
        base_preprocessing_layer.keras_kpl_gauge.get_cell(
            "RandomBrightness"
        ).set(True)
        super().__init__(seed=seed, force_generator=True, **kwargs)
        self._set_factor(factor)
        self._set_value_range(value_range)
        self._seed = seed

    def augment_image(self, image, transformation):
        return self._brightness_adjust(image, transformation["rgb_delta"])

    def augment_label(self, label, transformation):
        return label

    def get_random_transformation(
        self, image=None, label=None, bounding_box=None
    ):
        rgb_delta_shape = (1, 1, 1)
        random_rgb_delta = self._random_generator.random_uniform(
            shape=rgb_delta_shape,
            minval=self._factor[0],
            maxval=self._factor[1],
        )
        random_rgb_delta = random_rgb_delta * (
            self._value_range[1] - self._value_range[0]
        )
        return {"rgb_delta": random_rgb_delta}

    def _set_value_range(self, value_range):
        if not isinstance(value_range, (tuple, list)):
            raise ValueError(
                self._VALUE_RANGE_VALIDATION_ERROR + f"Got {value_range}"
            )
        if len(value_range) != 2:
            raise ValueError(
                self._VALUE_RANGE_VALIDATION_ERROR + f"Got {value_range}"
            )
        self._value_range = sorted(value_range)

    def _set_factor(self, factor):
        if isinstance(factor, (tuple, list)):
            if len(factor) != 2:
                raise ValueError(
                    self._FACTOR_VALIDATION_ERROR + f"Got {factor}"
                )
            self._check_factor_range(factor[0])
            self._check_factor_range(factor[1])
            self._factor = sorted(factor)
        elif isinstance(factor, (int, float)):
            self._check_factor_range(factor)
            factor = abs(factor)
            self._factor = [-factor, factor]
        else:
            raise ValueError(self._FACTOR_VALIDATION_ERROR + f"Got {factor}")

    def _check_factor_range(self, input_number):
        if input_number > 1.0 or input_number < -1.0:
            raise ValueError(
                self._FACTOR_VALIDATION_ERROR + f"Got {input_number}"
            )

    def _brightness_adjust(self, image, rgb_delta):
        image = utils.ensure_tensor(image, self.compute_dtype)
        rank = image.shape.rank
        if rank != 3:
            raise ValueError(
                "Expected the input image to be rank 3. Got "
                f"inputs.shape = {image.shape}"
            )
        rgb_delta = tf.cast(rgb_delta, image.dtype)
        image += rgb_delta
        return tf.clip_by_value(
            image, self._value_range[0], self._value_range[1]
        )

    def get_config(self):
        config = {
            "factor": self._factor,
            "value_range": self._value_range,
            "seed": self._seed,
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))


@keras_export(
    "keras.layers.RandomHeight",
    "keras.layers.experimental.preprocessing.RandomHeight",
    v1=[],
)
class RandomHeight(BaseImageAugmentationLayer):
    """A preprocessing layer which randomly varies image height during training.

    This layer adjusts the height of a batch of images by a random factor.
    The input should be a 3D (unbatched) or 4D (batched) tensor in the
    `"channels_last"` image data format. Input pixel values can be of any range
    (e.g. `[0., 1.)` or `[0, 255]`) and of interger or floating point dtype. By
    default, the layer will output floats.


    By default, this layer is inactive during inference.

    For an overview and full list of preprocessing layers, see the preprocessing
    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).

    Args:
      factor: A positive float (fraction of original height), or a tuple of size
        2 representing lower and upper bound for resizing vertically. When
        represented as a single float, this value is used for both the upper and
        lower bound. For instance, `factor=(0.2, 0.3)` results in an output with
        height changed by a random amount in the range `[20%, 30%]`.
        `factor=(-0.2, 0.3)` results in an output with height changed by a
        random amount in the range `[-20%, +30%]`. `factor=0.2` results in an
        output with height changed by a random amount in the range
        `[-20%, +20%]`.
      interpolation: String, the interpolation method. Defaults to `"bilinear"`.
        Supports `"bilinear"`, `"nearest"`, `"bicubic"`, `"area"`,
        `"lanczos3"`, `"lanczos5"`, `"gaussian"`, `"mitchellcubic"`.
      seed: Integer. Used to create a random seed.

    Input shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`, in `"channels_last"` format.

    Output shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., random_height, width, channels)`.
    """

    def __init__(self, factor, interpolation="bilinear", seed=None, **kwargs):
        base_preprocessing_layer.keras_kpl_gauge.get_cell("RandomHeight").set(
            True
        )
        super().__init__(seed=seed, force_generator=True, **kwargs)
        self.factor = factor
        if isinstance(factor, (tuple, list)):
            self.height_lower = factor[0]
            self.height_upper = factor[1]
        else:
            self.height_lower = -factor
            self.height_upper = factor

        if self.height_upper < self.height_lower:
            raise ValueError(
                "`factor` cannot have upper bound less than "
                "lower bound, got {}".format(factor)
            )
        if self.height_lower < -1.0 or self.height_upper < -1.0:
            raise ValueError(
                f"`factor` must have values larger than -1, got {factor}"
            )
        self.interpolation = interpolation
        self._interpolation_method = image_utils.get_interpolation(
            interpolation
        )
        self.seed = seed

    def get_random_transformation(
        self, image=None, label=None, bounding_box=None
    ):
        height_factor = self._random_generator.random_uniform(
            shape=[],
            minval=(1.0 + self.height_lower),
            maxval=(1.0 + self.height_upper),
        )
        inputs_shape = tf.shape(image)
        img_hd = tf.cast(inputs_shape[H_AXIS], tf.float32)
        adjusted_height = tf.cast(height_factor * img_hd, tf.int32)
        return {"height": adjusted_height}

    def _batch_augment(self, inputs):
        images = self.augment_image(
            inputs[IMAGES],
            transformation=self.get_random_transformation(image=inputs[IMAGES]),
        )
        result = {IMAGES: images}
        # to-do augment bbox to clip bbox to resized height value
        return result

    def augment_image(self, image, transformation):
        # The batch dimension of the input=image is not modified. The output
        # would be accurate for both unbatched and batched input
        inputs_shape = tf.shape(image)
        img_wd = inputs_shape[W_AXIS]
        adjusted_height = transformation["height"]
        adjusted_size = tf.stack([adjusted_height, img_wd])
        output = tf.image.resize(
            images=image, size=adjusted_size, method=self._interpolation_method
        )
        # tf.resize will output float32 in many cases regardless of input type.
        output = tf.cast(output, self.compute_dtype)
        output_shape = list(image.shape)
        output_shape[H_AXIS] = None
        output.set_shape(output_shape)
        return output

    def compute_output_shape(self, input_shape):
        input_shape = tf.TensorShape(input_shape).as_list()
        input_shape[H_AXIS] = None
        return tf.TensorShape(input_shape)

    def get_config(self):
        config = {
            "factor": self.factor,
            "interpolation": self.interpolation,
            "seed": self.seed,
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))


@keras_export(
    "keras.layers.RandomWidth",
    "keras.layers.experimental.preprocessing.RandomWidth",
    v1=[],
)
class RandomWidth(BaseImageAugmentationLayer):
    """A preprocessing layer which randomly varies image width during training.

    This layer will randomly adjusts the width of a batch of images of a
    batch of images by a random factor. The input should be a 3D (unbatched) or
    4D (batched) tensor in the `"channels_last"` image data format. Input pixel
    values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and of interger
    or floating point dtype. By default, the layer will output floats.

    By default, this layer is inactive during inference.

    For an overview and full list of preprocessing layers, see the preprocessing
    [guide](https://www.tensorflow.org/guide/keras/preprocessing_layers).

    Args:
      factor: A positive float (fraction of original width), or a tuple of size
        2 representing lower and upper bound for resizing vertically. When
        represented as a single float, this value is used for both the upper and
        lower bound. For instance, `factor=(0.2, 0.3)` results in an output with
        width changed by a random amount in the range `[20%, 30%]`.
        `factor=(-0.2, 0.3)` results in an output with width changed by a random
        amount in the range `[-20%, +30%]`. `factor=0.2` results in an output
        with width changed by a random amount in the range `[-20%, +20%]`.
      interpolation: String, the interpolation method. Defaults to `bilinear`.
        Supports `"bilinear"`, `"nearest"`, `"bicubic"`, `"area"`, `"lanczos3"`,
        `"lanczos5"`, `"gaussian"`, `"mitchellcubic"`.
      seed: Integer. Used to create a random seed.

    Input shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`, in `"channels_last"` format.

    Output shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, random_width, channels)`.
    """

    def __init__(self, factor, interpolation="bilinear", seed=None, **kwargs):
        base_preprocessing_layer.keras_kpl_gauge.get_cell("RandomWidth").set(
            True
        )
        super().__init__(seed=seed, force_generator=True, **kwargs)
        self.factor = factor
        if isinstance(factor, (tuple, list)):
            self.width_lower = factor[0]
            self.width_upper = factor[1]
        else:
            self.width_lower = -factor
            self.width_upper = factor
        if self.width_upper < self.width_lower:
            raise ValueError(
                "`factor` cannot have upper bound less than "
                "lower bound, got {}".format(factor)
            )
        if self.width_lower < -1.0 or self.width_upper < -1.0:
            raise ValueError(
                f"`factor` must have values larger than -1, got {factor}"
            )
        self.interpolation = interpolation
        self._interpolation_method = image_utils.get_interpolation(
            interpolation
        )
        self.seed = seed
        self.auto_vectorize = False

    def _batch_augment(self, inputs):
        images = self.augment_image(
            inputs[IMAGES],
            transformation=self.get_random_transformation(image=inputs[IMAGES]),
        )
        result = {IMAGES: images}
        # to-do augment bbox to clip bbox to resized width value
        return result

    def augment_image(self, image, transformation):
        # The batch dimension of the input=image is not modified. The output
        # would be accurate for both unbatched and batched input
        inputs = utils.ensure_tensor(image)
        inputs_shape = tf.shape(inputs)
        img_hd = inputs_shape[H_AXIS]
        adjusted_width = transformation["width"]
        adjusted_size = tf.stack([img_hd, adjusted_width])
        output = tf.image.resize(
            images=inputs, size=adjusted_size, method=self._interpolation_method
        )
        # tf.resize will output float32 in many cases regardless of input type.
        output = tf.cast(output, self.compute_dtype)
        output_shape = inputs.shape.as_list()
        output_shape[W_AXIS] = None
        output.set_shape(output_shape)
        return output

    def get_random_transformation(
        self, image=None, label=None, bounding_box=None
    ):
        inputs_shape = tf.shape(image)
        img_wd = tf.cast(inputs_shape[W_AXIS], tf.float32)
        width_factor = self._random_generator.random_uniform(
            shape=[],
            minval=(1.0 + self.width_lower),
            maxval=(1.0 + self.width_upper),
        )
        adjusted_width = tf.cast(width_factor * img_wd, tf.int32)
        return {"width": adjusted_width}

    def compute_output_shape(self, input_shape):
        input_shape = tf.TensorShape(input_shape).as_list()
        input_shape[W_AXIS] = None
        return tf.TensorShape(input_shape)

    def get_config(self):
        config = {
            "factor": self.factor,
            "interpolation": self.interpolation,
            "seed": self.seed,
        }
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))
